Overview

Dataset statistics

Number of variables29
Number of observations554822
Missing cells0
Missing cells (%)0.0%
Duplicate rows89598
Duplicate rows (%)16.1%
Total size in memory122.8 MiB
Average record size in memory232.0 B

Variable types

Numeric19
Categorical10

Alerts

Dataset has 89598 (16.1%) duplicate rowsDuplicates
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_first_bookingHigh correlation
days_from_account_created_until_first_booking is highly correlated with days_from_first_active_until_bookingHigh correlation
year_first_active is highly correlated with year_first_booking and 1 other fieldsHigh correlation
month_first_active is highly correlated with week_of_year_first_active and 4 other fieldsHigh correlation
day_first_active is highly correlated with day_account_createdHigh correlation
day_of_week_first_active is highly correlated with day_of _week_first_account_createdHigh correlation
week_of_year_first_active is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_first_booking is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
week_of_year_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_account_created is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
day_account_created is highly correlated with day_first_activeHigh correlation
day_of _week_first_account_created is highly correlated with day_of_week_first_activeHigh correlation
week_of _year_first_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_first_booking and 1 other fieldsHigh correlation
days_from_account_created_until_first_booking is highly correlated with days_from_first_active_until_booking and 1 other fieldsHigh correlation
year_first_active is highly correlated with year_first_booking and 1 other fieldsHigh correlation
month_first_active is highly correlated with week_of_year_first_active and 4 other fieldsHigh correlation
day_first_active is highly correlated with day_account_createdHigh correlation
day_of_week_first_active is highly correlated with day_of _week_first_account_createdHigh correlation
week_of_year_first_active is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_first_booking is highly correlated with days_from_first_active_until_booking and 3 other fieldsHigh correlation
month_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
week_of_year_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_account_created is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
day_account_created is highly correlated with day_first_activeHigh correlation
day_of _week_first_account_created is highly correlated with day_of_week_first_activeHigh correlation
week_of _year_first_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_first_bookingHigh correlation
days_from_account_created_until_first_booking is highly correlated with days_from_first_active_until_bookingHigh correlation
year_first_active is highly correlated with year_first_booking and 1 other fieldsHigh correlation
month_first_active is highly correlated with week_of_year_first_active and 4 other fieldsHigh correlation
day_first_active is highly correlated with day_account_createdHigh correlation
day_of_week_first_active is highly correlated with day_of _week_first_account_createdHigh correlation
week_of_year_first_active is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_first_booking is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
week_of_year_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_account_created is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
day_account_created is highly correlated with day_first_activeHigh correlation
day_of _week_first_account_created is highly correlated with day_of_week_first_activeHigh correlation
week_of _year_first_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
first_browser is highly correlated with first_device_typeHigh correlation
first_device_type is highly correlated with first_browser and 1 other fieldsHigh correlation
affiliate_channel is highly correlated with affiliate_providerHigh correlation
signup_app is highly correlated with first_device_typeHigh correlation
affiliate_provider is highly correlated with affiliate_channelHigh correlation
signup_flow is highly correlated with affiliate_channel and 3 other fieldsHigh correlation
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_first_booking and 4 other fieldsHigh correlation
days_from_account_created_until_first_booking is highly correlated with days_from_first_active_until_booking and 6 other fieldsHigh correlation
year_first_active is highly correlated with days_from_account_created_until_first_booking and 6 other fieldsHigh correlation
month_first_active is highly correlated with year_first_active and 6 other fieldsHigh correlation
day_first_active is highly correlated with day_first_booking and 1 other fieldsHigh correlation
day_of_week_first_active is highly correlated with day_of _week_first_account_createdHigh correlation
week_of_year_first_active is highly correlated with year_first_active and 6 other fieldsHigh correlation
year_first_booking is highly correlated with days_from_first_active_until_booking and 6 other fieldsHigh correlation
month_first_booking is highly correlated with days_from_first_active_until_booking and 7 other fieldsHigh correlation
day_first_booking is highly correlated with days_from_first_active_until_booking and 5 other fieldsHigh correlation
week_of_year_first_booking is highly correlated with days_from_first_active_until_booking and 8 other fieldsHigh correlation
year_account_created is highly correlated with days_from_account_created_until_first_booking and 6 other fieldsHigh correlation
month_account_created is highly correlated with year_first_active and 6 other fieldsHigh correlation
day_account_created is highly correlated with day_first_active and 1 other fieldsHigh correlation
day_of _week_first_account_created is highly correlated with day_of_week_first_activeHigh correlation
week_of _year_first_account_created is highly correlated with year_first_active and 6 other fieldsHigh correlation
affiliate_channel is highly correlated with signup_flow and 3 other fieldsHigh correlation
affiliate_provider is highly correlated with signup_flow and 2 other fieldsHigh correlation
first_affiliate_tracked is highly correlated with affiliate_channel and 1 other fieldsHigh correlation
signup_app is highly correlated with signup_flow and 3 other fieldsHigh correlation
first_device_type is highly correlated with signup_app and 1 other fieldsHigh correlation
first_browser is highly correlated with signup_flow and 2 other fieldsHigh correlation
days_from_first_active_until_account_created is highly skewed (γ1 = 67.77477362) Skewed
signup_flow has 441238 (79.5%) zeros Zeros
days_from_first_active_until_booking has 122446 (22.1%) zeros Zeros
days_from_first_active_until_account_created has 553802 (99.8%) zeros Zeros
days_from_account_created_until_first_booking has 122379 (22.1%) zeros Zeros
day_of_week_first_active has 86787 (15.6%) zeros Zeros
day_of_week_first_booking has 124075 (22.4%) zeros Zeros
day_of _week_first_account_created has 86744 (15.6%) zeros Zeros

Reproduction

Analysis started2022-07-22 14:01:59.557762
Analysis finished2022-07-22 14:08:12.378882
Duration6 minutes and 12.82 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

age
Real number (ℝ≥0)

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.77970232
Minimum16
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:12.713118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile23
Q128
median33
Q341
95-th percentile61
Maximum115
Range99
Interquartile range (IQR)13

Descriptive statistics

Standard deviation13.17440626
Coefficient of variation (CV)0.3581977402
Kurtosis6.791570217
Mean36.77970232
Median Absolute Deviation (MAD)6
Skewness2.111487904
Sum20406188
Variance173.5649802
MonotonicityNot monotonic
2022-07-22T11:08:12.901317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3030377
 
5.5%
3130259
 
5.5%
2929160
 
5.3%
3228602
 
5.2%
2827287
 
4.9%
3326616
 
4.8%
2724571
 
4.4%
3423755
 
4.3%
3522384
 
4.0%
2621436
 
3.9%
Other values (89)290375
52.3%
ValueCountFrequency (%)
1626
 
< 0.1%
1774
 
< 0.1%
181698
 
0.3%
193720
 
0.7%
202777
 
0.5%
214907
 
0.9%
228583
1.5%
2311942
2.2%
2414952
2.7%
2519088
3.4%
ValueCountFrequency (%)
11512
 
< 0.1%
1134
 
< 0.1%
1121
 
< 0.1%
1112
 
< 0.1%
110191
 
< 0.1%
109103
 
< 0.1%
10876
 
< 0.1%
10786
 
< 0.1%
10674
 
< 0.1%
1054126
0.7%

signup_flow
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.729837678
Minimum0
Maximum25
Zeros441238
Zeros (%)79.5%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:13.065878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.296777675
Coefficient of variation (CV)3.062008502
Kurtosis11.42426557
Mean1.729837678
Median Absolute Deviation (MAD)0
Skewness3.516556698
Sum959752
Variance28.05585374
MonotonicityNot monotonic
2022-07-22T11:08:13.209501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0441238
79.5%
128587
 
5.2%
223251
 
4.2%
2511500
 
2.1%
39959
 
1.8%
128254
 
1.5%
246593
 
1.2%
232840
 
0.5%
52139
 
0.4%
62036
 
0.4%
Other values (16)18425
 
3.3%
ValueCountFrequency (%)
0441238
79.5%
128587
 
5.2%
223251
 
4.2%
39959
 
1.8%
42027
 
0.4%
52139
 
0.4%
62036
 
0.4%
71827
 
0.3%
81841
 
0.3%
91668
 
0.3%
ValueCountFrequency (%)
2511500
2.1%
246593
1.2%
232840
 
0.5%
22841
 
0.2%
211045
 
0.2%
20699
 
0.1%
19734
 
0.1%
18710
 
0.1%
17709
 
0.1%
16712
 
0.1%

days_from_first_active_until_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1853
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.3910011
Minimum0
Maximum2228
Zeros122446
Zeros (%)22.1%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:13.377059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q3103
95-th percentile667
Maximum2228
Range2228
Interquartile range (IQR)102

Descriptive statistics

Standard deviation240.110073
Coefficient of variation (CV)2.080838807
Kurtosis9.851641831
Mean115.3910011
Median Absolute Deviation (MAD)6
Skewness2.981158886
Sum64021466
Variance57652.84716
MonotonicityNot monotonic
2022-07-22T11:08:13.553354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0122446
22.1%
170338
 
12.7%
232712
 
5.9%
320939
 
3.8%
415445
 
2.8%
511768
 
2.1%
610431
 
1.9%
79028
 
1.6%
87749
 
1.4%
96119
 
1.1%
Other values (1843)247847
44.7%
ValueCountFrequency (%)
0122446
22.1%
170338
12.7%
232712
 
5.9%
320939
 
3.8%
415445
 
2.8%
511768
 
2.1%
610431
 
1.9%
79028
 
1.6%
87749
 
1.4%
96119
 
1.1%
ValueCountFrequency (%)
22281
< 0.1%
20012
< 0.1%
19991
< 0.1%
19951
< 0.1%
19921
< 0.1%
19912
< 0.1%
19902
< 0.1%
19801
< 0.1%
19791
< 0.1%
19771
< 0.1%

days_from_first_active_until_account_created
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct324
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2266384534
Minimum0
Maximum1456
Zeros553802
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:13.739838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1456
Range1456
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.22598719
Coefficient of variation (CV)45.12026549
Kurtosis5722.187389
Mean0.2266384534
Median Absolute Deviation (MAD)0
Skewness67.77477362
Sum125744
Variance104.570814
MonotonicityNot monotonic
2022-07-22T11:08:13.934242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0553802
99.8%
2104
 
< 0.1%
191
 
< 0.1%
370
 
< 0.1%
456
 
< 0.1%
518
 
< 0.1%
615
 
< 0.1%
1611
 
< 0.1%
1110
 
< 0.1%
109
 
< 0.1%
Other values (314)636
 
0.1%
ValueCountFrequency (%)
0553802
99.8%
191
 
< 0.1%
2104
 
< 0.1%
370
 
< 0.1%
456
 
< 0.1%
518
 
< 0.1%
615
 
< 0.1%
77
 
< 0.1%
87
 
< 0.1%
99
 
< 0.1%
ValueCountFrequency (%)
14561
< 0.1%
13691
< 0.1%
13611
< 0.1%
11481
< 0.1%
10361
< 0.1%
10111
< 0.1%
10061
< 0.1%
9911
< 0.1%
9841
< 0.1%
9681
< 0.1%

days_from_account_created_until_first_booking
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1973
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.163858
Minimum-349
Maximum2001
Zeros122379
Zeros (%)22.1%
Negative240
Negative (%)< 0.1%
Memory size4.2 MiB
2022-07-22T11:08:14.174715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-349
5-th percentile0
Q11
median6
Q3103
95-th percentile665
Maximum2001
Range2350
Interquartile range (IQR)102

Descriptive statistics

Standard deviation239.8539224
Coefficient of variation (CV)2.082718716
Kurtosis9.866476349
Mean115.163858
Median Absolute Deviation (MAD)6
Skewness2.983115349
Sum63895442
Variance57529.90408
MonotonicityNot monotonic
2022-07-22T11:08:14.351275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0122379
22.1%
170291
 
12.7%
232699
 
5.9%
320925
 
3.8%
415431
 
2.8%
511773
 
2.1%
610439
 
1.9%
79029
 
1.6%
87752
 
1.4%
96119
 
1.1%
Other values (1963)247985
44.7%
ValueCountFrequency (%)
-3491
< 0.1%
-3471
< 0.1%
-3381
< 0.1%
-3081
< 0.1%
-2981
< 0.1%
-2951
< 0.1%
-2881
< 0.1%
-2731
< 0.1%
-2691
< 0.1%
-2681
< 0.1%
ValueCountFrequency (%)
20012
< 0.1%
19991
< 0.1%
19951
< 0.1%
19921
< 0.1%
19912
< 0.1%
19902
< 0.1%
19801
< 0.1%
19791
< 0.1%
19771
< 0.1%
19761
< 0.1%

year_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.692709
Minimum2009
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:14.494466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2011
Q12012
median2013
Q32013
95-th percentile2014
Maximum2014
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9277205842
Coefficient of variation (CV)0.000460935035
Kurtosis-0.3213344358
Mean2012.692709
Median Absolute Deviation (MAD)1
Skewness-0.368153838
Sum1116686194
Variance0.8606654823
MonotonicityNot monotonic
2022-07-22T11:08:14.619129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2013224563
40.5%
2012162933
29.4%
2014111093
20.0%
201150056
 
9.0%
20106168
 
1.1%
20099
 
< 0.1%
ValueCountFrequency (%)
20099
 
< 0.1%
20106168
 
1.1%
201150056
 
9.0%
2012162933
29.4%
2013224563
40.5%
2014111093
20.0%
ValueCountFrequency (%)
2014111093
20.0%
2013224563
40.5%
2012162933
29.4%
201150056
 
9.0%
20106168
 
1.1%
20099
 
< 0.1%

month_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.807932995
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:14.745031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.067627605
Coefficient of variation (CV)0.5281788905
Kurtosis-0.8693851241
Mean5.807932995
Median Absolute Deviation (MAD)2
Skewness0.2746715861
Sum3222369
Variance9.410339125
MonotonicityNot monotonic
2022-07-22T11:08:14.881423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
575346
13.6%
671619
12.9%
462369
11.2%
351841
9.3%
246169
8.3%
844135
8.0%
144064
7.9%
941154
7.4%
733804
6.1%
1032591
5.9%
Other values (2)51730
9.3%
ValueCountFrequency (%)
144064
7.9%
246169
8.3%
351841
9.3%
462369
11.2%
575346
13.6%
671619
12.9%
733804
6.1%
844135
8.0%
941154
7.4%
1032591
5.9%
ValueCountFrequency (%)
1219490
 
3.5%
1132240
5.8%
1032591
5.9%
941154
7.4%
844135
8.0%
733804
6.1%
671619
12.9%
575346
13.6%
462369
11.2%
351841
9.3%

day_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.51307807
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:15.028924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.421955108
Coefficient of variation (CV)0.5428938778
Kurtosis-1.178646631
Mean15.51307807
Median Absolute Deviation (MAD)7
Skewness-0.001275577787
Sum8606997
Variance70.92932785
MonotonicityNot monotonic
2022-07-22T11:08:15.184654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2220315
 
3.7%
2420284
 
3.7%
2320096
 
3.6%
1319804
 
3.6%
1619757
 
3.6%
2719726
 
3.6%
319643
 
3.5%
1519333
 
3.5%
1819184
 
3.5%
2119173
 
3.5%
Other values (21)357507
64.4%
ValueCountFrequency (%)
112964
2.3%
216335
2.9%
319643
3.5%
418862
3.4%
518939
3.4%
618732
3.4%
719010
3.4%
818728
3.4%
919097
3.4%
1019152
3.5%
ValueCountFrequency (%)
312858
 
0.5%
3011470
2.1%
2914889
2.7%
2817583
3.2%
2719726
3.6%
2618450
3.3%
2519050
3.4%
2420284
3.7%
2320096
3.6%
2220315
3.7%

day_of_week_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.439645508
Minimum0
Maximum6
Zeros86787
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:15.327092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.730416008
Coefficient of variation (CV)0.7092899368
Kurtosis-0.9192544035
Mean2.439645508
Median Absolute Deviation (MAD)1
Skewness0.274435407
Sum1353569
Variance2.994339561
MonotonicityNot monotonic
2022-07-22T11:08:15.440203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2107192
19.3%
1105135
18.9%
394722
17.1%
086787
15.6%
479003
14.2%
558026
10.5%
623957
 
4.3%
ValueCountFrequency (%)
086787
15.6%
1105135
18.9%
2107192
19.3%
394722
17.1%
479003
14.2%
558026
10.5%
623957
 
4.3%
ValueCountFrequency (%)
623957
 
4.3%
558026
10.5%
479003
14.2%
394722
17.1%
2107192
19.3%
1105135
18.9%
086787
15.6%

week_of_year_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.03364322
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:15.594366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q114
median23
Q334
95-th percentile48
Maximum53
Range52
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.31143732
Coefficient of variation (CV)0.5538668108
Kurtosis-0.8753861051
Mean24.03364322
Median Absolute Deviation (MAD)10
Skewness0.2732430855
Sum13334394
Variance177.1943636
MonotonicityNot monotonic
2022-07-22T11:08:15.780529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2318862
 
3.4%
2117613
 
3.2%
1917287
 
3.1%
2517251
 
3.1%
2417035
 
3.1%
2017028
 
3.1%
2616991
 
3.1%
1815764
 
2.8%
2215511
 
2.8%
1715159
 
2.7%
Other values (43)386321
69.6%
ValueCountFrequency (%)
15896
1.1%
26530
1.2%
310144
1.8%
410302
1.9%
59530
1.7%
611530
2.1%
711450
2.1%
811682
2.1%
911470
2.1%
1011924
2.1%
ValueCountFrequency (%)
532
 
< 0.1%
523305
 
0.6%
515236
0.9%
506236
1.1%
497096
1.3%
486394
1.2%
477579
1.4%
468339
1.5%
458227
1.5%
446880
1.2%

year_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.999165
Minimum2010
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:15.930132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2011
Q12012
median2013
Q32014
95-th percentile2015
Maximum2015
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.113669869
Coefficient of variation (CV)0.0005532391111
Kurtosis-0.3835469917
Mean2012.999165
Median Absolute Deviation (MAD)1
Skewness0.006615507521
Sum1116856223
Variance1.240260577
MonotonicityNot monotonic
2022-07-22T11:08:16.056827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2013202756
36.5%
2012137610
24.8%
2014110797
20.0%
201559671
 
10.8%
201138972
 
7.0%
20105016
 
0.9%
ValueCountFrequency (%)
20105016
 
0.9%
201138972
 
7.0%
2012137610
24.8%
2013202756
36.5%
2014110797
20.0%
201559671
 
10.8%
ValueCountFrequency (%)
201559671
 
10.8%
2014110797
20.0%
2013202756
36.5%
2012137610
24.8%
201138972
 
7.0%
20105016
 
0.9%

month_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.899670525
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:16.249349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.796555175
Coefficient of variation (CV)0.4740188732
Kurtosis-0.5926744518
Mean5.899670525
Median Absolute Deviation (MAD)2
Skewness0.2059007341
Sum3273267
Variance7.820720845
MonotonicityNot monotonic
2022-07-22T11:08:16.374980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6123243
22.2%
567843
12.2%
452103
9.4%
345645
 
8.2%
843818
 
7.9%
740280
 
7.3%
240204
 
7.2%
938519
 
6.9%
132810
 
5.9%
1030383
 
5.5%
Other values (2)39974
 
7.2%
ValueCountFrequency (%)
132810
 
5.9%
240204
 
7.2%
345645
 
8.2%
452103
9.4%
567843
12.2%
6123243
22.2%
740280
 
7.3%
843818
 
7.9%
938519
 
6.9%
1030383
 
5.5%
ValueCountFrequency (%)
1213310
 
2.4%
1126664
 
4.8%
1030383
 
5.5%
938519
 
6.9%
843818
 
7.9%
740280
 
7.3%
6123243
22.2%
567843
12.2%
452103
9.4%
345645
 
8.2%

day_first_booking
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.55563226
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:16.534588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median17
Q325
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.882690308
Coefficient of variation (CV)0.536535855
Kurtosis-1.249593904
Mean16.55563226
Median Absolute Deviation (MAD)8
Skewness-0.07880975466
Sum9185429
Variance78.90218711
MonotonicityNot monotonic
2022-07-22T11:08:16.698118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2966907
 
12.1%
1518764
 
3.4%
518441
 
3.3%
1618380
 
3.3%
1718161
 
3.3%
2318084
 
3.3%
417854
 
3.2%
1117826
 
3.2%
917689
 
3.2%
1317606
 
3.2%
Other values (21)325110
58.6%
ValueCountFrequency (%)
113073
2.4%
215027
2.7%
316969
3.1%
417854
3.2%
518441
3.3%
617026
3.1%
716618
3.0%
816778
3.0%
917689
3.2%
1017263
3.1%
ValueCountFrequency (%)
312045
 
0.4%
307912
 
1.4%
2966907
12.1%
2814280
 
2.6%
2715244
 
2.7%
2615743
 
2.8%
2517490
 
3.2%
2417523
 
3.2%
2318084
 
3.3%
2217539
 
3.2%

day_of_week_first_booking
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.200473305
Minimum0
Maximum6
Zeros124075
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:16.833819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.738417998
Coefficient of variation (CV)0.7900200355
Kurtosis-0.9319808208
Mean2.200473305
Median Absolute Deviation (MAD)1
Skewness0.3440031114
Sum1220871
Variance3.022097137
MonotonicityNot monotonic
2022-07-22T11:08:16.945140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0124075
22.4%
2101856
18.4%
197008
17.5%
390899
16.4%
474110
13.4%
550230
9.1%
616644
 
3.0%
ValueCountFrequency (%)
0124075
22.4%
197008
17.5%
2101856
18.4%
390899
16.4%
474110
13.4%
550230
9.1%
616644
 
3.0%
ValueCountFrequency (%)
616644
 
3.0%
550230
9.1%
474110
13.4%
390899
16.4%
2101856
18.4%
197008
17.5%
0124075
22.4%

week_of_year_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.67367372
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:17.103721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median25
Q333
95-th percentile46
Maximum52
Range51
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.19191992
Coefficient of variation (CV)0.4941266572
Kurtosis-0.6307787703
Mean24.67367372
Median Absolute Deviation (MAD)9
Skewness0.154249366
Sum13689497
Variance148.6429112
MonotonicityNot monotonic
2022-07-22T11:08:17.296568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2766367
 
12.0%
2417626
 
3.2%
2317089
 
3.1%
2116538
 
3.0%
2515858
 
2.9%
2615322
 
2.8%
2014949
 
2.7%
1914893
 
2.7%
2213908
 
2.5%
1813788
 
2.5%
Other values (42)348484
62.8%
ValueCountFrequency (%)
14203
 
0.8%
24831
0.9%
37850
1.4%
48075
1.5%
57387
1.3%
69146
1.6%
79753
1.8%
810274
1.9%
99929
1.8%
1010616
1.9%
ValueCountFrequency (%)
521808
 
0.3%
513927
0.7%
504824
0.9%
495719
1.0%
485329
1.0%
475931
1.1%
466672
1.2%
457077
1.3%
445760
1.0%
436278
1.1%

year_account_created
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2013
224619 
2012
162945 
2014
111134 
2011
49995 
2010
 
6129

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2219288
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011
2nd row2010
3rd row2011
4th row2010
5th row2010

Common Values

ValueCountFrequency (%)
2013224619
40.5%
2012162945
29.4%
2014111134
20.0%
201149995
 
9.0%
20106129
 
1.1%

Length

2022-07-22T11:08:17.479086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-22T11:08:18.398366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2013224619
40.5%
2012162945
29.4%
2014111134
20.0%
201149995
 
9.0%
20106129
 
1.1%

Most occurring characters

ValueCountFrequency (%)
2717767
32.3%
1604817
27.3%
0560951
25.3%
3224619
 
10.1%
4111134
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2219288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2717767
32.3%
1604817
27.3%
0560951
25.3%
3224619
 
10.1%
4111134
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common2219288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2717767
32.3%
1604817
27.3%
0560951
25.3%
3224619
 
10.1%
4111134
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2219288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2717767
32.3%
1604817
27.3%
0560951
25.3%
3224619
 
10.1%
4111134
 
5.0%

month_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.809180242
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:18.537029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.068355187
Coefficient of variation (CV)0.5281907359
Kurtosis-0.8703270809
Mean5.809180242
Median Absolute Deviation (MAD)2
Skewness0.2740977688
Sum3223061
Variance9.414803552
MonotonicityNot monotonic
2022-07-22T11:08:18.659738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
575316
13.6%
671584
12.9%
462329
11.2%
351820
9.3%
246147
8.3%
844152
8.0%
144083
7.9%
941159
7.4%
733820
6.1%
1032590
5.9%
Other values (2)51822
9.3%
ValueCountFrequency (%)
144083
7.9%
246147
8.3%
351820
9.3%
462329
11.2%
575316
13.6%
671584
12.9%
733820
6.1%
844152
8.0%
941159
7.4%
1032590
5.9%
ValueCountFrequency (%)
1219495
 
3.5%
1132327
5.8%
1032590
5.9%
941159
7.4%
844152
8.0%
733820
6.1%
671584
12.9%
575316
13.6%
462329
11.2%
351820
9.3%

day_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.51491109
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:18.802495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.422993585
Coefficient of variation (CV)0.5428966713
Kurtosis-1.178968574
Mean15.51491109
Median Absolute Deviation (MAD)7
Skewness-0.001614584424
Sum8608014
Variance70.94682094
MonotonicityNot monotonic
2022-07-22T11:08:18.958117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2220325
 
3.7%
2420306
 
3.7%
2320060
 
3.6%
1319783
 
3.6%
1619780
 
3.6%
2719739
 
3.6%
319649
 
3.5%
1519324
 
3.5%
1819185
 
3.5%
1019165
 
3.5%
Other values (21)357506
64.4%
ValueCountFrequency (%)
112969
2.3%
216337
2.9%
319649
3.5%
418866
3.4%
518931
3.4%
618713
3.4%
719002
3.4%
818730
3.4%
919114
3.4%
1019165
3.5%
ValueCountFrequency (%)
312857
 
0.5%
3011463
2.1%
2914883
2.7%
2817651
3.2%
2719739
3.6%
2618450
3.3%
2519054
3.4%
2420306
3.7%
2320060
3.6%
2220325
3.7%

day_of _week_first_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.439924877
Minimum0
Maximum6
Zeros86744
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:19.107375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.730548632
Coefficient of variation (CV)0.7092630795
Kurtosis-0.9196450026
Mean2.439924877
Median Absolute Deviation (MAD)1
Skewness0.2744991132
Sum1353724
Variance2.994798568
MonotonicityNot monotonic
2022-07-22T11:08:19.217083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2107256
19.3%
1105165
19.0%
394577
17.0%
086744
15.6%
479052
14.2%
558060
10.5%
623968
 
4.3%
ValueCountFrequency (%)
086744
15.6%
1105165
19.0%
2107256
19.3%
394577
17.0%
479052
14.2%
558060
10.5%
623968
 
4.3%
ValueCountFrequency (%)
623968
 
4.3%
558060
10.5%
479052
14.2%
394577
17.0%
2107256
19.3%
1105165
19.0%
086744
15.6%

week_of _year_first_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.03956044
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-07-22T11:08:19.377677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q114
median23
Q334
95-th percentile48
Maximum53
Range52
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.31524146
Coefficient of variation (CV)0.5538887242
Kurtosis-0.8765677966
Mean24.03956044
Median Absolute Deviation (MAD)10
Skewness0.2726658434
Sum13337677
Variance177.2956552
MonotonicityNot monotonic
2022-07-22T11:08:19.575199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2318859
 
3.4%
2117617
 
3.2%
1917269
 
3.1%
2517245
 
3.1%
2417028
 
3.1%
2016997
 
3.1%
2616985
 
3.1%
1815766
 
2.8%
2215523
 
2.8%
1715159
 
2.7%
Other values (43)386374
69.6%
ValueCountFrequency (%)
15898
1.1%
26531
1.2%
310142
1.8%
410303
1.9%
59540
1.7%
611533
2.1%
711455
2.1%
811676
2.1%
911466
2.1%
1011914
2.1%
ValueCountFrequency (%)
532
 
< 0.1%
523306
 
0.6%
515237
0.9%
506237
1.1%
497098
1.3%
486396
1.2%
477642
1.4%
468357
1.5%
458232
1.5%
446888
1.2%

gender
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
FEMALE
246792 
MALE
220601 
-unknown-
85802 
OTHER
 
1627

Length

Max length9
Median length6
Mean length5.665797319
Min length4

Characters and Unicode

Total characters3143509
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowFEMALE
3rd rowFEMALE
4th row-unknown-
5th rowFEMALE

Common Values

ValueCountFrequency (%)
FEMALE246792
44.5%
MALE220601
39.8%
-unknown-85802
 
15.5%
OTHER1627
 
0.3%

Length

2022-07-22T11:08:19.754810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-22T11:08:19.915455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
female246792
44.5%
male220601
39.8%
unknown85802
 
15.5%
other1627
 
0.3%

Most occurring characters

ValueCountFrequency (%)
E715812
22.8%
M467393
14.9%
A467393
14.9%
L467393
14.9%
n257406
 
8.2%
F246792
 
7.9%
-171604
 
5.5%
u85802
 
2.7%
k85802
 
2.7%
o85802
 
2.7%
Other values (5)92310
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2371291
75.4%
Lowercase Letter600614
 
19.1%
Dash Punctuation171604
 
5.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E715812
30.2%
M467393
19.7%
A467393
19.7%
L467393
19.7%
F246792
 
10.4%
O1627
 
0.1%
T1627
 
0.1%
H1627
 
0.1%
R1627
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
n257406
42.9%
u85802
 
14.3%
k85802
 
14.3%
o85802
 
14.3%
w85802
 
14.3%
Dash Punctuation
ValueCountFrequency (%)
-171604
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2971905
94.5%
Common171604
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E715812
24.1%
M467393
15.7%
A467393
15.7%
L467393
15.7%
n257406
 
8.7%
F246792
 
8.3%
u85802
 
2.9%
k85802
 
2.9%
o85802
 
2.9%
w85802
 
2.9%
Other values (4)6508
 
0.2%
Common
ValueCountFrequency (%)
-171604
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3143509
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E715812
22.8%
M467393
14.9%
A467393
14.9%
L467393
14.9%
n257406
 
8.2%
F246792
 
7.9%
-171604
 
5.5%
u85802
 
2.7%
k85802
 
2.7%
o85802
 
2.7%
Other values (5)92310
 
2.9%

signup_method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
basic
348063 
facebook
206245 
google
 
514

Length

Max length8
Median length5
Mean length6.116121927
Min length5

Characters and Unicode

Total characters3393359
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfacebook
2nd rowbasic
3rd rowfacebook
4th rowbasic
5th rowbasic

Common Values

ValueCountFrequency (%)
basic348063
62.7%
facebook206245
37.2%
google514
 
0.1%

Length

2022-07-22T11:08:20.074244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-22T11:08:20.296649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
basic348063
62.7%
facebook206245
37.2%
google514
 
0.1%

Most occurring characters

ValueCountFrequency (%)
b554308
16.3%
a554308
16.3%
c554308
16.3%
o413518
12.2%
s348063
10.3%
i348063
10.3%
e206759
 
6.1%
f206245
 
6.1%
k206245
 
6.1%
g1028
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3393359
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b554308
16.3%
a554308
16.3%
c554308
16.3%
o413518
12.2%
s348063
10.3%
i348063
10.3%
e206759
 
6.1%
f206245
 
6.1%
k206245
 
6.1%
g1028
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin3393359
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b554308
16.3%
a554308
16.3%
c554308
16.3%
o413518
12.2%
s348063
10.3%
i348063
10.3%
e206759
 
6.1%
f206245
 
6.1%
k206245
 
6.1%
g1028
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3393359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b554308
16.3%
a554308
16.3%
c554308
16.3%
o413518
12.2%
s348063
10.3%
i348063
10.3%
e206759
 
6.1%
f206245
 
6.1%
k206245
 
6.1%
g1028
 
< 0.1%

language
Categorical

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
en
539744 
fr
 
3573
es
 
2504
de
 
2425
zh
 
1775
Other values (20)
 
4801

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1109644
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen

Common Values

ValueCountFrequency (%)
en539744
97.3%
fr3573
 
0.6%
es2504
 
0.5%
de2425
 
0.4%
zh1775
 
0.3%
it1047
 
0.2%
ko970
 
0.2%
ru706
 
0.1%
pt380
 
0.1%
nl375
 
0.1%
Other values (15)1323
 
0.2%

Length

2022-07-22T11:08:20.491128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en539744
97.3%
fr3573
 
0.6%
es2504
 
0.5%
de2425
 
0.4%
zh1775
 
0.3%
it1047
 
0.2%
ko970
 
0.2%
ru706
 
0.1%
pt380
 
0.1%
nl375
 
0.1%
Other values (15)1323
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e544766
49.1%
n540193
48.7%
r4392
 
0.4%
f3605
 
0.3%
s2860
 
0.3%
d2557
 
0.2%
h1811
 
0.2%
z1775
 
0.2%
t1555
 
0.1%
i1105
 
0.1%
Other values (9)5025
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1109644
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e544766
49.1%
n540193
48.7%
r4392
 
0.4%
f3605
 
0.3%
s2860
 
0.3%
d2557
 
0.2%
h1811
 
0.2%
z1775
 
0.2%
t1555
 
0.1%
i1105
 
0.1%
Other values (9)5025
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin1109644
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e544766
49.1%
n540193
48.7%
r4392
 
0.4%
f3605
 
0.3%
s2860
 
0.3%
d2557
 
0.2%
h1811
 
0.2%
z1775
 
0.2%
t1555
 
0.1%
i1105
 
0.1%
Other values (9)5025
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1109644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e544766
49.1%
n540193
48.7%
r4392
 
0.4%
f3605
 
0.3%
s2860
 
0.3%
d2557
 
0.2%
h1811
 
0.2%
z1775
 
0.2%
t1555
 
0.1%
i1105
 
0.1%
Other values (9)5025
 
0.5%

affiliate_channel
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
direct
369212 
sem-brand
74104 
sem-non-brand
48327 
seo
 
24541
other
 
16198
Other values (3)
 
22440

Length

Max length13
Median length6
Mean length6.807776188
Min length3

Characters and Unicode

Total characters3777104
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowseo
2nd rowdirect
3rd rowdirect
4th rowdirect
5th rowother

Common Values

ValueCountFrequency (%)
direct369212
66.5%
sem-brand74104
 
13.4%
sem-non-brand48327
 
8.7%
seo24541
 
4.4%
other16198
 
2.9%
api13988
 
2.5%
content5726
 
1.0%
remarketing2726
 
0.5%

Length

2022-07-22T11:08:20.705561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-22T11:08:20.976986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
direct369212
66.5%
sem-brand74104
 
13.4%
sem-non-brand48327
 
8.7%
seo24541
 
4.4%
other16198
 
2.9%
api13988
 
2.5%
content5726
 
1.0%
remarketing2726
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e543560
14.4%
r513293
13.6%
d491643
13.0%
t399588
10.6%
i385926
10.2%
c374938
9.9%
n233263
6.2%
-170758
 
4.5%
s146972
 
3.9%
a139145
 
3.7%
Other values (7)378018
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3606346
95.5%
Dash Punctuation170758
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e543560
15.1%
r513293
14.2%
d491643
13.6%
t399588
11.1%
i385926
10.7%
c374938
10.4%
n233263
6.5%
s146972
 
4.1%
a139145
 
3.9%
m125157
 
3.5%
Other values (6)252861
7.0%
Dash Punctuation
ValueCountFrequency (%)
-170758
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3606346
95.5%
Common170758
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e543560
15.1%
r513293
14.2%
d491643
13.6%
t399588
11.1%
i385926
10.7%
c374938
10.4%
n233263
6.5%
s146972
 
4.1%
a139145
 
3.9%
m125157
 
3.5%
Other values (6)252861
7.0%
Common
ValueCountFrequency (%)
-170758
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3777104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e543560
14.4%
r513293
13.6%
d491643
13.0%
t399588
10.6%
i385926
10.2%
c374938
9.9%
n233263
6.2%
-170758
 
4.5%
s146972
 
3.9%
a139145
 
3.7%
Other values (7)378018
10.0%

affiliate_provider
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
direct
368194 
google
139247 
other
 
22501
facebook
 
6916
craigslist
 
6349
Other values (12)
 
11615

Length

Max length19
Median length6
Mean length6.051560681
Min length3

Characters and Unicode

Total characters3357539
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgoogle
2nd rowdirect
3rd rowdirect
4th rowdirect
5th rowcraigslist

Common Values

ValueCountFrequency (%)
direct368194
66.4%
google139247
 
25.1%
other22501
 
4.1%
facebook6916
 
1.2%
craigslist6349
 
1.1%
bing5678
 
1.0%
facebook-open-graph1739
 
0.3%
vast1371
 
0.2%
padmapper972
 
0.2%
yahoo755
 
0.1%
Other values (7)1100
 
0.2%

Length

2022-07-22T11:08:21.297132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
direct368194
66.4%
google139247
 
25.1%
other22501
 
4.1%
facebook6916
 
1.2%
craigslist6349
 
1.1%
bing5678
 
1.0%
facebook-open-graph1739
 
0.3%
vast1371
 
0.2%
padmapper972
 
0.2%
yahoo755
 
0.1%
Other values (7)1100
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e542628
16.2%
r400096
11.9%
t399031
11.9%
i387137
11.5%
c383198
11.4%
d369213
11.0%
o321554
9.6%
g292887
8.7%
l145863
 
4.3%
h24995
 
0.7%
Other values (14)90937
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3353794
99.9%
Dash Punctuation3745
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e542628
16.2%
r400096
11.9%
t399031
11.9%
i387137
11.5%
c383198
11.4%
d369213
11.0%
o321554
9.6%
g292887
8.7%
l145863
 
4.3%
h24995
 
0.7%
Other values (13)87192
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
-3745
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3353794
99.9%
Common3745
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e542628
16.2%
r400096
11.9%
t399031
11.9%
i387137
11.5%
c383198
11.4%
d369213
11.0%
o321554
9.6%
g292887
8.7%
l145863
 
4.3%
h24995
 
0.7%
Other values (13)87192
 
2.6%
Common
ValueCountFrequency (%)
-3745
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3357539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e542628
16.2%
r400096
11.9%
t399031
11.9%
i387137
11.5%
c383198
11.4%
d369213
11.0%
o321554
9.6%
g292887
8.7%
l145863
 
4.3%
h24995
 
0.7%
Other values (14)90937
 
2.7%

first_affiliate_tracked
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
untracked
303301 
linked
121916 
omg
112953 
tracked-other
 
12471
product
 
3857
Other values (2)
 
324

Length

Max length13
Median length9
Mean length7.195284253
Min length3

Characters and Unicode

Total characters3992102
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuntracked
2nd rowuntracked
3rd rowuntracked
4th rowuntracked
5th rowuntracked

Common Values

ValueCountFrequency (%)
untracked303301
54.7%
linked121916
22.0%
omg112953
 
20.4%
tracked-other12471
 
2.2%
product3857
 
0.7%
marketing241
 
< 0.1%
local ops83
 
< 0.1%

Length

2022-07-22T11:08:21.474656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-22T11:08:21.662157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
untracked303301
54.7%
linked121916
22.0%
omg112953
 
20.4%
tracked-other12471
 
2.2%
product3857
 
0.7%
marketing241
 
< 0.1%
local83
 
< 0.1%
ops83
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e450400
11.3%
d441545
11.1%
k437929
11.0%
n425458
10.7%
t332341
8.3%
r332341
8.3%
c319712
8.0%
a316096
7.9%
u307158
7.7%
o129447
 
3.2%
Other values (9)499675
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3979548
99.7%
Dash Punctuation12471
 
0.3%
Space Separator83
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e450400
11.3%
d441545
11.1%
k437929
11.0%
n425458
10.7%
t332341
8.4%
r332341
8.4%
c319712
8.0%
a316096
7.9%
u307158
7.7%
o129447
 
3.3%
Other values (7)487121
12.2%
Dash Punctuation
ValueCountFrequency (%)
-12471
100.0%
Space Separator
ValueCountFrequency (%)
83
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3979548
99.7%
Common12554
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e450400
11.3%
d441545
11.1%
k437929
11.0%
n425458
10.7%
t332341
8.4%
r332341
8.4%
c319712
8.0%
a316096
7.9%
u307158
7.7%
o129447
 
3.3%
Other values (7)487121
12.2%
Common
ValueCountFrequency (%)
-12471
99.3%
83
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3992102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e450400
11.3%
d441545
11.1%
k437929
11.0%
n425458
10.7%
t332341
8.3%
r332341
8.3%
c319712
8.0%
a316096
7.9%
u307158
7.7%
o129447
 
3.2%
Other values (9)499675
12.5%

signup_app
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Web
512969 
iOS
 
28268
Moweb
 
7764
Android
 
5821

Length

Max length7
Median length3
Mean length3.069953967
Min length3

Characters and Unicode

Total characters1703278
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeb
2nd rowWeb
3rd rowWeb
4th rowWeb
5th rowWeb

Common Values

ValueCountFrequency (%)
Web512969
92.5%
iOS28268
 
5.1%
Moweb7764
 
1.4%
Android5821
 
1.0%

Length

2022-07-22T11:08:21.989280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-22T11:08:22.256599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
web512969
92.5%
ios28268
 
5.1%
moweb7764
 
1.4%
android5821
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e520733
30.6%
b520733
30.6%
W512969
30.1%
i34089
 
2.0%
O28268
 
1.7%
S28268
 
1.7%
o13585
 
0.8%
d11642
 
0.7%
M7764
 
0.5%
w7764
 
0.5%
Other values (3)17463
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1120188
65.8%
Uppercase Letter583090
34.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e520733
46.5%
b520733
46.5%
i34089
 
3.0%
o13585
 
1.2%
d11642
 
1.0%
w7764
 
0.7%
n5821
 
0.5%
r5821
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
W512969
88.0%
O28268
 
4.8%
S28268
 
4.8%
M7764
 
1.3%
A5821
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1703278
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e520733
30.6%
b520733
30.6%
W512969
30.1%
i34089
 
2.0%
O28268
 
1.7%
S28268
 
1.7%
o13585
 
0.8%
d11642
 
0.7%
M7764
 
0.5%
w7764
 
0.5%
Other values (3)17463
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1703278
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e520733
30.6%
b520733
30.6%
W512969
30.1%
i34089
 
2.0%
O28268
 
1.7%
S28268
 
1.7%
o13585
 
0.8%
d11642
 
0.7%
M7764
 
0.5%
w7764
 
0.5%
Other values (3)17463
 
1.0%

first_device_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Mac Desktop
287424 
Windows Desktop
184099 
iPad
35356 
iPhone
32453 
Other/Unknown
 
6024
Other values (4)
 
9466

Length

Max length18
Median length11
Mean length11.66327038
Min length4

Characters and Unicode

Total characters6471039
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMac Desktop
2nd rowWindows Desktop
3rd rowMac Desktop
4th rowMac Desktop
5th rowMac Desktop

Common Values

ValueCountFrequency (%)
Mac Desktop287424
51.8%
Windows Desktop184099
33.2%
iPad35356
 
6.4%
iPhone32453
 
5.8%
Other/Unknown6024
 
1.1%
Desktop (Other)3714
 
0.7%
Android Phone3066
 
0.6%
Android Tablet2620
 
0.5%
SmartPhone (Other)66
 
< 0.1%

Length

2022-07-22T11:08:22.459654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-22T11:08:22.767830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
desktop475237
45.9%
mac287424
27.7%
windows184099
 
17.8%
ipad35356
 
3.4%
iphone32453
 
3.1%
other/unknown6024
 
0.6%
android5686
 
0.5%
other3780
 
0.4%
phone3066
 
0.3%
tablet2620
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o706631
10.9%
s659336
10.2%
e523246
 
8.1%
t487727
 
7.5%
k481261
 
7.4%
480989
 
7.4%
D475237
 
7.3%
p475237
 
7.3%
a325466
 
5.0%
M287424
 
4.4%
Other values (20)1568485
24.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4934565
76.3%
Uppercase Letter1041901
 
16.1%
Space Separator480989
 
7.4%
Other Punctuation6024
 
0.1%
Open Punctuation3780
 
0.1%
Close Punctuation3780
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o706631
14.3%
s659336
13.4%
e523246
10.6%
t487727
9.9%
k481261
9.8%
p475237
9.6%
a325466
6.6%
c287424
5.8%
i257594
 
5.2%
n243442
 
4.9%
Other values (7)487201
9.9%
Uppercase Letter
ValueCountFrequency (%)
D475237
45.6%
M287424
27.6%
W184099
 
17.7%
P70941
 
6.8%
O9804
 
0.9%
U6024
 
0.6%
A5686
 
0.5%
T2620
 
0.3%
S66
 
< 0.1%
Space Separator
ValueCountFrequency (%)
480989
100.0%
Other Punctuation
ValueCountFrequency (%)
/6024
100.0%
Open Punctuation
ValueCountFrequency (%)
(3780
100.0%
Close Punctuation
ValueCountFrequency (%)
)3780
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5976466
92.4%
Common494573
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o706631
11.8%
s659336
11.0%
e523246
8.8%
t487727
 
8.2%
k481261
 
8.1%
D475237
 
8.0%
p475237
 
8.0%
a325466
 
5.4%
M287424
 
4.8%
c287424
 
4.8%
Other values (16)1267477
21.2%
Common
ValueCountFrequency (%)
480989
97.3%
/6024
 
1.2%
(3780
 
0.8%
)3780
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII6471039
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o706631
10.9%
s659336
10.2%
e523246
 
8.1%
t487727
 
7.5%
k481261
 
7.4%
480989
 
7.4%
D475237
 
7.3%
p475237
 
7.3%
a325466
 
5.0%
M287424
 
4.4%
Other values (20)1568485
24.2%

first_browser
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Chrome
193752 
Safari
136078 
Firefox
100149 
IE
45748 
Mobile Safari
42756 
Other values (36)
36339 

Length

Max length18
Median length6
Mean length6.611925987
Min length2

Characters and Unicode

Total characters3668442
Distinct characters46
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowChrome
2nd rowIE
3rd rowFirefox
4th rowChrome
5th rowSafari

Common Values

ValueCountFrequency (%)
Chrome193752
34.9%
Safari136078
24.5%
Firefox100149
18.1%
IE45748
 
8.2%
Mobile Safari42756
 
7.7%
-unknown-31231
 
5.6%
Chrome Mobile2144
 
0.4%
Android Browser1143
 
0.2%
Opera417
 
0.1%
Chromium286
 
0.1%
Other values (31)1118
 
0.2%

Length

2022-07-22T11:08:23.051588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chrome195896
32.6%
safari178834
29.7%
firefox100188
16.7%
ie45761
 
7.6%
mobile44954
 
7.5%
unknown31231
 
5.2%
browser1236
 
0.2%
android1143
 
0.2%
opera421
 
0.1%
chromium286
 
< 0.1%
Other values (33)1435
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r480009
13.1%
o375836
10.2%
a358450
9.8%
e343556
9.4%
i325739
8.9%
f279022
 
7.6%
m196478
 
5.4%
h196267
 
5.4%
C196239
 
5.3%
S179086
 
4.9%
Other values (36)737760
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2942613
80.2%
Uppercase Letter616793
 
16.8%
Dash Punctuation62462
 
1.7%
Space Separator46563
 
1.3%
Other Punctuation11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r480009
16.3%
o375836
12.8%
a358450
12.2%
e343556
11.7%
i325739
11.1%
f279022
9.5%
m196478
6.7%
h196267
6.7%
x100541
 
3.4%
n95071
 
3.2%
Other values (14)191644
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
C196239
31.8%
S179086
29.0%
F100208
16.2%
E46023
 
7.5%
I45871
 
7.4%
M45291
 
7.3%
A1493
 
0.2%
B1373
 
0.2%
O663
 
0.1%
L240
 
< 0.1%
Other values (9)306
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-62462
100.0%
Space Separator
ValueCountFrequency (%)
46563
100.0%
Other Punctuation
ValueCountFrequency (%)
.11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3559406
97.0%
Common109036
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r480009
13.5%
o375836
10.6%
a358450
10.1%
e343556
9.7%
i325739
9.2%
f279022
7.8%
m196478
 
5.5%
h196267
 
5.5%
C196239
 
5.5%
S179086
 
5.0%
Other values (33)628724
17.7%
Common
ValueCountFrequency (%)
-62462
57.3%
46563
42.7%
.11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3668442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r480009
13.1%
o375836
10.2%
a358450
9.8%
e343556
9.4%
i325739
8.9%
f279022
 
7.6%
m196478
 
5.4%
h196267
 
5.4%
C196239
 
5.3%
S179086
 
4.9%
Other values (36)737760
20.1%

Interactions

2022-07-22T11:07:57.434000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:05:54.932909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:02.973551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:09.667639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:16.689797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:22.928799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:29.593754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:36.554628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:42.775139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:49.818105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:56.141318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:03.542122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:10.087647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:16.608975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:23.495159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:29.902010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:37.478532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:43.915323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:51.081793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:57.757137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:05:56.101211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:03.298931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:09.993820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:16.996976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:23.240476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:30.076461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:36.867823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:43.085284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:50.149218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:56.454059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:03.859309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:10.396199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:17.053457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:23.810325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:30.234888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:37.795684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:44.251391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:51.393775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:58.081294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:05:56.670476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:03.626177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:10.304195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:17.338926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:23.549605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:30.513803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:37.174150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:43.414457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:50.496322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:56.786138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:04.175481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:10.769725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:17.467350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:24.139252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:30.570049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:38.146612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:44.588946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:51.726316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:58.425011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:05:57.160939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:03.921667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:10.673517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:17.685689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:23.862767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:30.848483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:37.480299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:43.775000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:50.818567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:57.092352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:04.498650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:11.088873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:17.963060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:24.478799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:30.876233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:38.513601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:44.923388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:52.031466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:58.789039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:05:57.661603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:04.254780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:11.011703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:18.019238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:24.180915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:31.576292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:37.829850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:44.261699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:51.148411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:57.432003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:04.919512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:11.433949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:18.444771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:24.825396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:31.207347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:38.860191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:45.258998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:52.376578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:59.107156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-22T11:07:15.093993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:22.144724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:28.532321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:35.640935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:42.538663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:49.390490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:56.104224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:08:02.870794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:01.989640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:08.639336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:15.688988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:21.963198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:28.220253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:35.526157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:41.829539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:48.829720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:55.097393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:02.567727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:09.101846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:15.416628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:22.515764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:28.886839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:35.995986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:42.887289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:49.869720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:56.435853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:08:03.200911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:02.303151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:08.989307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:16.039051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:22.276366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:28.717922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:35.901247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:42.139276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:49.140857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:55.437040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:02.895858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:09.429720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:15.761650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:22.841094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:29.206983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:36.322957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:43.212298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:50.368388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:56.762978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:08:03.571519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:02.650899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:09.313583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:16.351701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:22.603529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:29.140790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:36.220292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:42.454401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:49.465056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:06:55.780723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:03.224286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:09.767712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:16.149204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:23.163521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:29.545079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:37.153402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:43.538499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:50.762639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-22T11:07:57.091400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-22T11:08:23.373013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-22T11:08:24.301131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-22T11:08:24.958684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-22T11:08:25.340975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-22T11:08:25.602318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-22T11:08:04.970198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-22T11:08:08.637648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

agesignup_flowdays_from_first_active_until_bookingdays_from_first_active_until_account_createddays_from_account_created_until_first_bookingyear_first_activemonth_first_activeday_first_activeday_of_week_first_activeweek_of_year_first_activeyear_first_bookingmonth_first_bookingday_first_bookingday_of_week_first_bookingweek_of_year_first_bookingyear_account_createdmonth_account_createdday_account_createdday_of _week_first_account_createdweek_of _year_first_account_createdgendersignup_methodlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browser
038022287321496200952352120156290272011525221MALEfacebookenseogoogleuntrackedWebMac DesktopChrome
1563419476-572009691242010820312010928139FEMALEbasicendirectdirectuntrackedWebWindows DesktopIE
24201043765278200910315442012985362011125049FEMALEfacebookendirectdirectuntrackedWebMac DesktopFirefox
341072280-20820091281502010218372010914137-unknown-basicendirectdirectuntrackedWebMac DesktopChrome
446030320101255320101511201012553FEMALEbasicenothercraigslistuntrackedWebMac DesktopSafari
547010010201013653201011322201013653FEMALEbasicendirectdirectomgWebMac DesktopSafari
6500206020620101401201072933020101401FEMALEbasicenothercraigslistuntrackedWebMac DesktopSafari
7460000201014012010140120101401-unknown-basicenothercraigslistomgWebMac DesktopFirefox
8360202201014012010162120101401FEMALEbasicenothercraigslistuntrackedWebMac DesktopFirefox
947020010200120101511201562902720101511FEMALEbasicenothercraigslistuntrackedWebiPhone-unknown-

Last rows

agesignup_flowdays_from_first_active_until_bookingdays_from_first_active_until_account_createddays_from_account_created_until_first_bookingyear_first_activemonth_first_activeday_first_activeday_of_week_first_activeweek_of_year_first_activeyear_first_bookingmonth_first_bookingday_first_bookingday_of_week_first_bookingweek_of_year_first_bookingyear_account_createdmonth_account_createdday_account_createdday_of _week_first_account_createdweek_of _year_first_account_createdgendersignup_methodlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browser
554812460172017220141965201472723020141965MALEbasicendirectdirectuntrackedWebMac DesktopChrome
554813250000201312171512013121715120131217151MALEbasicensem-brandgoogleomgWebWindows DesktopChrome
5548142920730732014342112014516521201434211FEMALEbasicendirectdirectuntrackediOSMac DesktopChrome
554815310101201311191472013112024720131119147FEMALEbasicensem-non-brandgoogleomgWebWindows DesktopChrome
554816400101201210104020121022402012101040FEMALEbasicendirectdirectomgWebMac DesktopSafari
554817330101201332021220133224122013320212FEMALEbasicendirectdirectlinkedWebMac DesktopFirefox
554818340520522012770272012829335201277027MALEfacebookendirectdirectlinkedWebWindows DesktopFirefox
55481929022022201352122120136133242013521221-unknown-basicendirectdirectuntrackedWebWindows DesktopIE
554820320303201411303201411633201411303FEMALEbasicendirectdirectlinkedWebMac DesktopSafari
554821390101201172252920117233292011722529FEMALEbasicendirectdirectuntrackedWebWindows DesktopChrome

Duplicate rows

Most frequently occurring

agesignup_flowdays_from_first_active_until_bookingdays_from_first_active_until_account_createddays_from_account_created_until_first_bookingyear_first_activemonth_first_activeday_first_activeday_of_week_first_activeweek_of_year_first_activeyear_first_bookingmonth_first_bookingday_first_bookingday_of_week_first_bookingweek_of_year_first_bookingyear_account_createdmonth_account_createdday_account_createdday_of _week_first_account_createdweek_of _year_first_account_createdgendersignup_methodlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browser# duplicates
51421350000201361352720136135272013613527-unknown-basicendirectdirectuntrackedWebMac DesktopChrome89
33585310000201361552420136155242013615524FEMALEfacebookendirectdirectuntrackedWebWindows DesktopChrome85
43184330000201361562420136156242013615624-unknown-basicensem-brandgoogleuntrackedWebWindows DesktopChrome68
38479320000201361552420136155242013615524-unknown-basicensem-brandgoogleuntrackedWebWindows DesktopChrome66
28021300000201281423320128142332012814233FEMALEfacebookendirectdirectlinkedWebMac DesktopChrome65
11409260000201334010201335110201334010-unknown-basicendirectdirectuntrackedWebMac DesktopChrome64
47566340000201361562420136156242013615624-unknown-basicendirectdirectlinkedWebMac DesktopFirefox63
8336250000201335110201336210201335110MALEfacebookensem-brandgoogleomgWebWindows DesktopChrome62
43884330101201310334020131044402013103340MALEbasicendirectdirectuntrackedWebWindows DesktopChrome62
54619360000201261812420126181242012618124FEMALEfacebookendirectdirectomgWebMac DesktopSafari60